提出协同感知与预测新框架，提升复杂交通中车辆对周围物体的预判能力。
The Components of Collaborative Joint Perception and Prediction -- A Conceptual Framework
- 分两模块：场景补全与联合感知预测，便于部署和扩展。
- 通过共享数据缓解感知误差累积与视线遮挡问题。
- 适合自动驾驶系统研发者参考，尤其关注多车协同场景。
联网自动驾驶车辆（CAVs）得益于车联网（V2X）通信，可交换传感器数据以实现协同感知（CP）。为减少感知模块中的累积误差并缓解视觉遮挡问题，本文提出一项新任务——协同联合感知与预测（Co-P&P），并构建了其实现的概念框架，旨在提升周围物体的运动预测能力，从而增强车辆在复杂交通场景下的环境感知水平。该框架包含两个解耦的核心模块：协同场景补全（CSC）与联合感知与预测（P&P）模块，简化了实际部署并提升了可扩展性。此外，本文还概述了Co-P&P面临的挑战，并探讨了该研究方向的未来发展趋势。
原文摘要 · Abstract (English)
Connected Autonomous Vehicles (CAVs) benefit from Vehicle-to-Everything (V2X) communication, which enables the exchange of sensor data to achieve Collaborative Perception (CP). To reduce cumulative errors in perception modules and mitigate the visual occlusion, this paper introduces a new task, Collaborative Joint Perception and Prediction (Co-P&P), and provides a conceptual framework for its implementation to improve motion prediction of surrounding objects, thereby enhancing vehicle awareness in complex traffic scenarios. The framework consists of two decoupled core modules, Collaborative Scene Completion (CSC) and Joint Perception and Prediction (P&P) module, which simplify practical deployment and enhance scalability. Additionally, we outline the challenges in Co-P&P and discuss future directions for this research area.
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